Comprehensive Testing & Critical Bug Fixes

[aaronlippold]

Comprehensive Testing & Critical Bug Fixes

Overview

This PR adds comprehensive testing coverage and fixes two critical bugs, including one that causes permanent data loss during installation. We independently discovered and fixed the async/await issue addressed in v2.3.2, and we've integrated with that release while maintaining modern Python conventions.

Summary:

• Added 167 new tests (83 → 250 tests total)
• Fixed critical data loss bug in storage path handling
• Fixed async/await coroutine error (same as v2.3.2, discovered independently)
• Achieved 100% ruff compliance with modern Python conventions
• Cross-platform compatibility validated (Linux, macOS, Windows)

Critical Bug Fixes

1. Data Loss During Package Installation

Issue: Running uv pip install -e . caused permanent deletion of ALL memory slots without warning, resulting in complete loss of user data.

Root Cause:

# storage.py and archival.py used relative paths
self.memory_dir = Path("memory_slots")  # ❌ Resolves relative to cwd

When uv pip install -e . changes the working directory during installation, the relative path resolves to a different location, causing data to be written to (and potentially deleted from) the wrong directory.

Solution (storage.py:82-83, archival.py:82-83):

# Use absolute paths that work regardless of cwd
self.memory_dir = Path(memory_dir).expanduser().absolute()  # ✅ Always correct

Testing: 4 comprehensive regression tests in test_storage_path_bug.py validate the fix and prevent regression.

Note: v2.3.1 added valuable data protection utilities and documentation. Our fix complements that by addressing the root cause in the code, preventing the issue from occurring in the first place.

2. Async/Await Coroutine Error (v2.3.2 Compatibility)

Issue: TypeError: object of type 'coroutine' has no len() in health monitoring.

Discovery: We independently discovered and fixed this bug during our testing work. When we merged with upstream v2.3.2, we found you had released the same fix.

Our Solution:

• Made DiagnosticTool._check_storage_health() properly async
• Added await calls in run_health_checks() and generate_system_report()
• Fixed duplicate await self.run_health_checks() call (line 685)

Merge Resolution: We kept the modern Python type hints (list[T] vs List[T]) and clean formatting from our version while incorporating your v2.3.2 changes.

Testing: 12 comprehensive async health monitoring tests ensure this works correctly.

Testing Coverage

Module Coverage Summary

Module	Coverage	Tests	Focus
models.py	93%	39	Data validation, security contracts
status_monitoring.py	86%	12	Async health checks, diagnostics
cache.py	75%	22	LRU/disk caching, TTL expiration
storage.py	73%	44	Complete CRUD + PATCH API
server.py	52%	69	All 23 MCP tool handlers
optimized_server.py	54%	18	Token optimization

Overall: 43% coverage focused on critical user-facing functionality

Testing Methodology

Bottom-up approach:

1. Models & validation (foundation)
2. Cache layer (performance)
3. Storage layer (persistence)
4. Server layer (integration)

Focus areas:

• Public API behavior (not implementation details)
• Real behavior validation (tested actual API, not assumptions)
• Complete CRUD + PATCH coverage
• Edge cases and error handling
• Concurrent operations

Example: We tested the actual behavior to discover that save_memory() replaces content while add_summary_entry() appends, then wrote tests accordingly.

Test Infrastructure

New test files:

• test_cache.py (22 tests) - Multi-level caching with Windows compatibility
• test_storage_operations.py (44 tests) - Complete storage API coverage
• test_server_mcp_interface.py (69 tests) - All MCP tool handlers
• test_optimized_server.py (18 tests) - Token usage optimization
• test_storage_path_bug.py (4 tests) - Data loss bug regression tests

Enhanced infrastructure:

• conftest.py - Fixtures, factories, helpers for consistent testing
• test_models_contracts.py - +17 tests for validation and security

Code Quality & Modernization

Ruff Compliance (100%)

• Fixed 74 linting issues systematically (UP038, B904, F841, E501, etc.)
• Modern Python type hints (PEP 604): list[T], dict[K,V] instead of List[T], Dict[K,V]
• Modern exception chaining with from e
• Removed unused imports, variables, redundant code

Code Improvements

• DRY refactoring: Eliminated 32 redundant fixture assignments
• Direct fixture usage in component tests
• Clean, maintainable test patterns

CI/CD

• Added GitHub Actions workflows following MCP Python SDK standards
• Cross-platform testing (Ubuntu, Windows, macOS)
• Python 3.10, 3.11, 3.12 compatibility
• Ruff format/check validation

Cross-Platform Compatibility

All 250 tests pass on:

• ✅ Linux (Ubuntu latest)
• ✅ macOS (latest)
• ✅ Windows (latest) - added compatibility fixes for:
  • Datetime precision in cache tests
  • File handling during cleanup operations

Integration with v2.3.2

We merged with upstream v2.3.2 and resolved conflicts by:

• Keeping modern Python type hints throughout
• Using clean formatting (single blank lines vs extra whitespace)
• Fixing duplicate await self.run_health_checks() call
• Maintaining our additional testing and quality improvements

The async/await fix we made matches yours, discovered independently during our testing work.

Files Changed

Source code (31 files):

• Bug fixes: storage.py, archival.py, status_monitoring.py
• Code quality: Ruff compliance across all source files

Tests (13 files):

• New: 4 test modules
• Enhanced: 9 existing test modules
• Infrastructure: conftest.py with fixtures/factories

Configuration (4 files):

• .github/workflows/ - CI/CD following MCP SDK standards
• .gitignore - Test artifacts and internal docs
• pyproject.toml - Dev dependencies (merged with v2.3.2)

Acknowledgments

Thank you for creating and maintaining memcord! We use it extensively in our work and wanted to contribute back with testing coverage and bug fixes. Your v2.3.1 data protection utilities and v2.3.2 async fixes show great attention to quality and user safety.

These changes aim to build on memcord's solid foundation by adding comprehensive testing and modernizing the codebase with current Python best practices.

Please let us know if you'd like any adjustments or have suggestions for improvements!